Aerosol particles are ubiquitous in the atmosphere and affect the
quality of human life through their climatic and health effects. The
formation and growth of aerosol particles involve extremely complex reactions
and processes. Due to limited research tools, the sources and chemistry of
aerosols are still not fully understood, and until now have normally
been investigated by using chemical species of secondary aerosols (e.g.,
NH4+, NO3-, SO42-, SOC) as tracers.
Here we investigated the role of silicon (Si), an ubiquitous but relatively
inert element, during the secondary aerosol formation process. We analyzed
the correlation of Si in airborne fine particles (PM2.5) collected
in Beijing – a typical pollution region – with the secondary chemical
species and secondary particle precursors (e.g., SO2 and
NOx). The total mass of Si in PM2.5 was found to
be uncorrelated with the secondary aerosol formation process, which suggested
that Si is a new conservative tracer for the amount of primary materials in
PM2.5 and can be used to estimate the relative amount of secondary
and primary compounds in PM2.5. This finding enables the accurate
estimation of secondary aerosol contribution to PM2.5 by using Si
as a single tracer rather than the commonly used multiple chemical tracers. In
addition, we show that the correlation analysis of secondary aerosols with
the Si isotopic composition of PM2.5 can further reveal
the sources of the precursors of secondary aerosols. Therefore, Si may
provide a new tool for aerosol chemistry studies.

Atmospheric particulate pollution is a global environmental issue that
seriously threatens human health and sustainable development. The fine
particulate matter less than 2.5 µm (PM2.5) in size is of
great concern because it poses significant risks to human health and is a
major cause of the haze phenomenon (Pope et al., 2009, 2002).
Understanding the sources and chemistry of aerosols is critical for air
pollution control. The aerosol particles can be directly emitted from
primary sources (primary particles) or secondarily formed from gaseous
precursors (e.g., SO2, NOx, NH3, and volatile organic
compounds – VOCs) through complex chemical reactions and processes in the
atmosphere (secondary particles) (Zhang et al., 2012b, 2015). Particularly,
secondary aerosol (SA) is of great importance as it
contributes to a large portion of particulate pollution in most pollution
regions (e.g., China and India) (Huang et al., 2014). However, until now,
the formation mechanism of secondary aerosols has not been fully understood and it
is difficult to accurately estimate secondary aerosols due to the extreme
complexity of aerosol chemistry and limited research tools. The traditional
method is based on the combined estimates of secondary inorganic aerosol
(SIA) and secondary organic aerosol (SOA) using multiple chemical components
indicative of secondary chemistry, e.g., NH4+, NO3-, and
SO42- for SIA (Bi et al., 2007), and organic
carbon (OC) and elemental carbon (EC) for SOA (Docherty et al., 2008).
However, the accuracy of the method is still controversial and measurements
of these multiple tracers are laborious (Schmid et al., 2001; Watson et
al., 2005).

As the second highest abundant element in the Earth's crust, silicon (Si) is
ubiquitous in terrestrial systems, as well as in atmospheric aerosols. The
roles of Si in continental and marine environments have been well clarified
in biogeochemical studies (Basile-Doelsch, 2006). However, despite the ubiquitousness of Si, the research on atmospheric Si is
rather limited (Bzdek et al., 2014; Lu et al., 2018). We note that Si is
a special element compared with other high-abundance ones (e.g., O, C, N,
and S). It is relatively inert, normally forms bonds only with oxygen
(in SiIV), and does not form volatile compounds in the natural
environment (Savage et al., 2014). In fact, over 90 % of Si in the
Earth's crust is composed of nonvolatile silicate minerals that should not
be able to serve as gaseous precursors for secondary aerosols (an exception
is synthetic organosilicons that will be discussed later). Therefore, Si may
provide a different route to aerosol chemistry from that using traditional
tracers.

Figure 1Scheme showing the role of Si during the secondary
formation process of aerosol particles. Note: this scheme only reflects the role
of Si but does not show all reactions involved in the secondary aerosol
formation.

In this study, we aimed to clarify the role of Si during the secondary
aerosol formation process in the atmosphere. Based on the nature of Si, we
hypothesize that Si in PM2.5 is unaffected by the secondary particle
formation process and predominantly emitted from primary sources (as
depicted in Fig. 1). Therefore, the particular properties of Si (i.e., high
abundance and chemical inactiveness) may make it a new conservative tracer
for aerosol chemistry studies. To specify this point, we herein show the
estimation of secondary aerosols based on the dilution effect of Si during
the secondary aerosol formation process by using Si as a single tracer, and
we compare this with the traditional method that uses multiple chemical tracers.

2.1 Chemicals and reagents

The standard reference material of urban atmospheric particulate matter
(NIST-1648a) and the Si isotope standard (NIST SRM-8546) were purchased from
the National Institute of Standards and Technology (Gaithersburg, MD). The
Si isotope standard IRMM-017 was purchased from the Institute for Reference
Materials and Measurements (GEEL, Belgium). The element calibration standard
solution was from Agilent (Santa Clara, CA). Sodium hydroxide was from
Beijing Ruikai Electronic Co. (Beijing, China). Nitric acid was from Merck (Darmstadt,
Germany). Hydrochloric acid was from Beijing Chemical Works (Beijing,
China). Hydrogen peroxide was from Sinopharm Chemical Reagent Co. (Shanghai,
China). Ultrapure water (18.3 MΩ⋅cm) obtained from a
Milli-Q gradient system (Millipore, Bedford) was used.

2.2 Sampling of PM2.5 samples

The PM2.5 and primary source samples were collected around the Beijing
region, China, as described in a previous report (Lu et al., 2018).
Briefly, the PM2.5 samples were collected in an urban district of
Beijing in 2013 by using a low-volume air sampler (Partisol 2025i, Thermo
Fisher, USA) at a flow rate of 16.7 L min−1. The sampling site was ca. 20 m
above the ground and surrounded by office and residential buildings
(40.0481∘ N, 116.4254∘ E). We selected the days with
relatively high PM2.5 concentrations in each week in 2013 (n=63) to
reflect the haze pollution condition of the whole year. Several haze events
over consecutive days were also included to investigate the SA formation
process. The sampling dates with the PM2.5 concentrations and
meteorological parameters are detailed in Table S1 in the Supplement. The
PM2.5 samples were collected onto Whatman 3–5 Teflon membrane filters
(Ø = 47 mm, Maidstone, UK) or Munktell MK360 quartz filters
(Ø = 90 mm, Maidstone, UK) and weighed by the giant gravimetric balance method
(Yang et al., 2015). Different filters were chosen depending on
target analytes, e.g., the analysis of Si and water soluble inorganic ions
must be performed with Teflon filters, while quartz filters should be used
for OC and EC analysis.

2.3 Sample preparation procedures

For the analysis of Si concentration and isotopic composition, the samples
were digested and purified as reported previously
(Georg et al., 2006; Zambardi and Poitrasson,
2011). Briefly, the sample was first dried in a silver crucible in a muffle
furnace at 1000 K for 10 min. Then, high-purity solid NaOH was added to the
sample at a ratio of 1:20 and the mixture was heated at 1000 K for another
10 min, followed by cooling down to room temperature. The obtained fusion
cake was dissolved with 2 mL of water followed by 24 h incubation, and then
the solution was acidized by an HCl solution to pH ∼ 2.

To eliminate the interference from the matrix, the samples were purified by
cation-exchange column chromatography. The cation resin (Dowex 50WX8,
200–400 mesh) in H+ form was packed in to a 1.8 mL resin bed in a
BioRad column and then rinsed with HCl, HNO3 solution, and water
until the eluate reached pH ∼ 7. Afterwards, 1 mL of the
sample solution was loaded to the resin and eluted with 2 mL of water.
Because Si in the solution is present mostly in the form of nonionic
Si(OH)4 and anionic H3SiO4- (Georg et al., 2006),
cationic species could be removed from Si species by the cation-exchange
resin. The recovery during the column purification procedures with the
IRMM-017 was >95 %. The recovery of Si during the whole sample
preparation procedures with the NIST-1648 was 89.9 %–96.2 %.

2.4 Measurement of Si concentration

The concentration of Si was measured on an Agilent 8800 inductively coupled
plasma mass spectrometer (Santa Clara, CA, USA). The clean blank membrane
filters have also been analyzed using the same procedures to subtract the
background signals from the samples.

2.5 Estimation of secondary aerosols in PM2.5 by using the Si-dilution method

Providing the total Si in PM2.5 (SiPM2.5) remains unchanged
during the secondary growth of PM2.5, we can obtain

(1)mPM2.5=mpri+ms,(2)SiPM2.5=mPM2.5×CPM2.5=mpri×Cpri,

where mPM2.5, mpri, and ms represent the mass of PM2.5,
primary particles, and secondary particles, respectively; SiPM2.5
represent the total Si mass in PM2.5; CPM2.5 and Cpri represent
Si abundance in PM2.5 and primary particles, respectively. Thus, the
contribution of secondary aerosols to PM2.5 (fs) can be estimated
by the Eq. (3):

(3)fs=msmPM2.5=1-CPM2.5Cpri.

The CPM2.5 can be obtained by direct measurement of the collected
PM2.5 samples, and the Cpri can be estimated from the mixed Si
abundances of primary sources:

(4)Cpri=∑(Ci×fi),

where Ci and fi represent the Si abundance and mass fraction of
different primary sources (fi=mi/∑mi). In
Eq. (4), the Ci can be obtained by measuring the collected primary
source samples. In order to obtain fi, we need to know the emission
amount (mi) of each source. The mi of anthropogenic sources in the
Beijing region was adopted from the Multi-resolution Emission Inventory for
China (MEIC) database (Li et al., 2017). For the mi of natural
sources (i.e., dust), it can be calculated based on the isotopic mass
balance of Si in PM2.5:

(5)δ30SiPM2.5=δ30Sipri=∑δ30Sii×Ci×fi/Cpri,

where δ30SiPM2.5 represents Si isotopic composition of
PM2.5 and δ30Sii represents Si isotopic signatures of
different primary sources. The mi values for different primary sources
used in the calculation are given in Table 1.

Table 1Parameters for primary sources used in the estimation of
secondary aerosol contribution to PM2.5.

a Data from the MEIC database (MEIC). b Since the
MEIC database does not include data for biomass burning,
the mi of biomass burning was estimated from the data of other sources
according to source apportionment results of 2013 reported previously (Tian et al., 2016).
c In the calculation, soil, construction, and urban fugitive dust are
treated as a single source considering their similarity in Si abundance and
Si isotopic signatures (Lu et al., 2018). The mi of dust was
calculated by using isotopic mass balance of Si in PM2.5 (see
Methodology).
d The Si abundance and natural Si isotopic signatures of primary
sources around Beijing are adopted from a previous study (Lu et al.,
2018).

2.6 Uncertainty analysis in the estimation of secondary aerosols

The uncertainty of secondary aerosol contribution estimated by the
Si-dilution method was obtained by the error propagation calculation. The
general formula for the error propagation calculation is as follows (Ku,
1966; Larsen et al., 2012):

(6)Sf=∑∂f∂xi×Si2,

where Sf represents the standard deviation of the function
f, xi represents the variables in the function
f, and Si represents the standard deviation of xi. The
Eq. (6) was applied to each step of the calculation of secondary aerosol
contribution (fs) to obtain the uncertainty of the final result.
Briefly, the uncertainty of fs is dependent on several variables in
the calculation, including the measured Si abundance in PM2.5
(CPM2.5), Si abundance (Ci) and mass fraction of primary sources
(fi), and Si isotopic composition of PM2.5 (δ30SiPM2.5) and primary sources (δ30Sii). The
errors of CPM2.5, Ci, δ30SiPM2.5, and δ30Sii can be directly obtained in the experimental measurements.
The fi is given by the MEIC database (MEIC; Li et al., 2017). Note
that the MEIC is a public emission inventory database that has been widely
applied and well validated in air pollution research in China (Jiang et
al., 2015; Zheng et al., 2015; Guan et al., 2014; Lin et al., 2014; Zhang et
al., 2012a), but it does not include error information for the data. So, the
error of fi was not included in the calculation. In this way, the
uncertainty of the annual mean fs in 2013 was calculated to be
26.1 %. This value included both the method uncertainty and the variations
on different dates. Nevertheless, it should be noted that the emission inventory
actually affected the uncertainty of the estimate result. For example, if
the uncertainty of the emission inventory was assumed to be 5 %, the
uncertainty of the annual mean fs in 2013 would be increased to
29.3 %. On the other hand, the MEIC database used here can only provide
yearly emission mass data, so using a high temporally resolved emission
inventory may further increase the accuracy of the result.

3.1 Correlation of Si with the secondary species in PM2.5

To verify the role of Si as depicted in Fig. 1, we analyzed Si abundance and
secondary aerosol tracers (e.g., NH4+, NO3-,
SO42-, OC, and EC) in PM2.5 samples collected around Beijing,
China, a typical particulate pollution region, on haze days (n=63) in
2013, in which year the particulate pollution in this region reached an
unprecedentedly high level (Huang et al., 2014). We found that the
PM2.5 concentration showed a clear seasonal trend (higher in
spring–winter than in summer–autumn) within the range of
21.7–337.1 µg m−3 (Fig. S1 and Table S1 in the Supplement). The PM2.5 profile of the
year 2013 was consistent with that reported previously (Lu et al.,
2018), confirming the representativeness of the data. All secondary species in
PM2.5 showed a similar seasonal trend with the PM2.5 concentration
(Fig. S1). However, the total Si in PM2.5 (expressed as
SiPM2.5 relative to air volume in µg m−3) did not show any
seasonal trends (Fig. S2).

Figure 2Correlation analysis of PM2.5 concentration and its
Si content (SiPM2.5) with the secondary species in PM2.5.
(a–d). Correlation of PM2.5 concentration with
SO42-(a), NO3-(b), NH4+(c), and SOC (d). (e–h). Correlation of
SiPM2.5 with SO42-(e), NO3-(f),
NH4+(g), and SOC (h).

Furthermore, Fig. 2 shows the correlation of PM2.5 and its Si content
with the secondary species in PM2.5. It can be seen that the PM2.5
concentration was highly linearly correlated with the secondary species (P<0.01).
These chemical species are directly indicative of secondary
chemistry: sulfate is mainly converted from atmospheric SO2 primarily
emitted from coal combustion (Seinfeld and Pandis, 2016), nitrate
originates from NOx emitted mainly from vehicle exhaust and power
plants (Seinfeld and Pandis, 2016), and secondary organic carbon
(SOC), as an indicator of SOA, derives from complex gaseous precursors
(Hallquist et al., 2009). Specifically, PM2.5 showed a higher
correlation with SIA (P<0.001) than with SOA (P<0.01),
probably due to the higher contribution of SIA to SA than SOA (Fig. S3). However, as a stark contrast to the significant correlation in
Fig. 2a–d, the SiPM2.5 showed no significant correlation with any of
the secondary species (P>0.14); additionally, no obvious trend in
SiPM2.5 was observed with the increase of secondary species. These
results strongly evidenced that the Si was inactive during the secondary
chemical process of aerosols.

3.2 Correlation of Si with the secondary precursors and relative humidity

To further demonstrate the role of Si in secondary aerosols, we analyzed the
correlation of SiPM2.5 with the secondary precursors
(SO2 and NOx) and relative humidity (RH). RH is an
important meteorological parameter that affects the secondary aerosol
formation (Tang et al., 2016). As shown in Fig. 3a–c, PM2.5 was
positively correlated with the atmospheric concentration of SO2 and
NOx (P<0.001) and RH (P<0.05). It is worth noting that all
PM2.5 samples yielded an OC ∕ EC ratio >2 (Fig. 3d), which
indicated the generation of secondary aerosols (Chow et al., 1994), while
SiPM2.5 was not significantly correlated with any of the
secondary precursors or RH (P>0.3; Fig. 3e–g), again demonstrating that
the SiPM2.5 was not affected by the secondary chemistry of
aerosols. Furthermore, we also found that the SiPM2.5 was not
correlated with the PM2.5 concentration (Fig. S4).

3.3 Effects of synthetic organosilicons and dry deposition

Based on the aforementioned results, we infer that Si should mainly be
present in primary particles or act as nuclei during the formation of
secondary particles. This was consistent with the nonvolatile nature of
most Si-containing compounds in the natural environment. An exception is
synthetic low-mass organic Si compounds (e.g., methylsiloxanes; Schweigkofler and Niessner, 1999; Ahrens et al., 2014; Xu et al.,
2012), which may be able to transform into aerosol nanoparticles via hydroxyl-radical-mediated oxidative cleavage of Si-C bonds in the upper atmosphere
(Atkinson, 1991; Sommerlade et al., 1993; Atkinson et al., 1995; Wu and
Johnston, 2017). However, previous studies on the impact of low-mass
organosilicons on the air quality have demonstrated that organosilicons did
not contribute to the lower atmosphere aerosol formation (Graiver et al.,
2003; Sommerlade et al., 1993). As a result, the US Environmental
Protection Agency (EPA) has excluded organosilicons from the regulation
concerning restriction of VOCs in the atmosphere. Recently, Janechek et
al. (2017) found that the oxidative product concentration of cyclic volatile
methylsiloxanes was ∼ 10–40 times lower than their parent
compounds in the US (Janechek et al., 2017). Another
study by Wu and Johnston reported that secondary aerosol yields of siloxane
oxidation might reach 15 % (Wu and Johnston, 2017), suggesting
that the contribution of organosilicons to secondary aerosols might vary in
different regions. Thus, it may be necessary to consider the secondary Si
production to determine whether it should be included in the secondary
aerosol calculation. Note that the secondary Si production depends on the
atmospheric concentration of both organosilicons and hydroxyl radicals. Up
to now, no data about atmospheric organosilicons in the studied region were
reported. Despite that, some recent studies reported the measurement and
modeling results of atmospheric hydroxyl radicals in the North China Plain
(Tan et al., 2017; Tham et al., 2016). The daily maximum concentration of
atmospheric hydroxyl radicals was in the range of (5–15) × 106 cm−3 (∼ 10−10 mol m−3). Since hydroxyl
radical is a crucial condition for the siloxane oxidation, the extremely low
concentration of hydroxyl radicals in the atmosphere could greatly limit the
secondary Si production from siloxanes in the studied region, suggesting
that the secondary Si production could be negligible compared with the
high-abundance Si in primary particles. This deduction has also been
experimentally verified by the irrelevance of SiPM2.5 with the
secondary chemistry of aerosols in Figs. 2 and 3.

We have also considered the influence of dry deposition on the Si abundance
in PM2.5. The dry deposition is a size-dependent process that can cause
coarse particles to fall more quickly than fine particles. However, it was
found that the average Si abundance did not change significantly when the
particle size was smaller than 2.5 µm (Tan et al., 2016). That is
to say, for fine particles (<2.5µm), the dry deposition rate
would not significantly affect the Si abundance. Thus, the Si-dilution
effect with PM2.5 should be predominantly controlled by the secondary
aerosol formation.

Figure 4Comparison of the SA estimate results between the
Si-dilution method and the traditional method. (a) A flow chart
showing how the fs was obtained by the Si-dilution method. The boxes
with estimations from emission inventories are labeled with asterisk.
(b) Annual mean SA contribution to PM2.5 in 2013 in Beijing.
(c) Seasonal mean SA contribution in 2013 in Beijing. The error
bars for the Si-dilution method include both method uncertainty and the
variations of samples on different dates (see Sect. 2.6), while for the
traditional method the error bars only represent the variations of samples
on different dates (without method uncertainty). Please see text for a more
detailed discussion on the uncertainties. (d–e) Analysis
of SA contribution during a special haze episode. (d) Monitoring
of PM2.5 concentration variations during 16 to 17 October 2013. (e) Daily SA contributions during the episode estimated
by using the two different methods.

3.4 Secondary aerosol estimation using the Si-dilution method

The special role of Si in PM2.5 enables us to gain insights into the
secondary particles in PM2.5 by using Si as a new conservative tracer.
Here, as an example, we show the estimation of secondary aerosol
contribution to PM2.5, which was thought of as a complicated and difficult
task. Providing the SiPM2.5 remains unchanged during the secondary
growth of PM2.5, the secondary formation can cause a dilution of the Si
abundance in PM2.5. Thus, it is possible to estimate the secondary
aerosol contribution to PM2.5 (fs) simply by the dilution factor
(see Eq. 3 in Sect. 2.5). To this end, the theoretical Si abundance in
primary particles (Cpri) and the final Si abundance in PM2.5
(CPM2.5) need to be known. The CPM2.5 can be directly measured with
the collected PM2.5 samples. In this study, an annual mean value of
1.56 % (n=63) was obtained. The Cpri can be derived from the
mixed Si abundance of primary sources based on individual Si abundance and
emission amounts of primary sources: the former can be measured with the
collected primary source samples, and the latter can be given by a public
emission inventory (e.g., the MEIC database). It should be noted that the
emission inventory does not include the emission amount of natural primary
sources (i.e., dust) (MEIC), which can be estimated by the isotopic
mass balance of Si in PM2.5 based on the emission data of other sources
and Si isotopic signatures of primary sources (see Table 1) (Lu et
al., 2018). All estimation procedures are shown in Fig. 4a. In this
way, the theoretical mean Si abundance in primary particles was calculated
to be 7.51 %. That is, the theoretical Si abundance in primary particles
(7.51 %) was diluted by the secondary aerosol formation to be 1.56 %.
Based on these values, the mean contribution of secondary particles to
PM2.5 on haze days in 2013 in the studied region was easily calculated
to be 79.2 % (Fig. 4b). In the same way, we have also estimated the
secondary aerosol contribution to PM2.5 in different seasons (Fig. 4c).

Note that the MEIC database only provides yearly emission data of primary
sources. Thus, using a higher temporally resolved emission inventory in
future studies may further increase the accuracy of the result. In addition,
the interregional atmospheric transport of aerosols may also affect the
estimate result. Here the PM2.5 was assumed to mainly derive from local
emission of primary particles and SA precursors. In fact, a portion of it
might also come from adjacent cities and regions via atmospheric transport,
which could cause uncertainties in the result. Therefore, using primary
source information and emission inventory covering adjacent cities and regions
in the calculation may also improve the accuracy of the result.

On the other hand, VOCs including siloxanes are likely to become
increasingly significant in the SA formation as aerosols originating from
fossil fuels may become less important over time
(McDonald et al., 2018). The method present here is
based on the dilution effect of Si in primary particles during the SA
formation, and therefore it should be suitable for the environment where
secondary Si production is minimal (e.g., in heavily polluted urban regions
with a large fossil fuel contribution like Beijing). Nevertheless, the
method also remains flexible enough to be modified to be applicable in the
cases in which the secondary Si production is not negligible. In that case, the
secondary Si production mass needs to be estimated and included in the mass
balance calculations (Sect. 2.5).

3.5 Uncertainty analysis and comparison with the traditional method

The uncertainty of the estimate result could be obtained by the error
propagation calculation (Ku, 1966; Larsen et al., 2012) from the errors of
variables used in the calculation (see Sect. 2.6 for details). In this
way, the uncertainty of the annual mean SA contribution in 2013 was
calculated to be 26.1 %. This value included the method uncertainty and
the variations on different dates but did not include the effect of the emission
inventory because the MEIC database does not include error information for
the data (see Sect. 2.6). Thus, the effect of the uncertainty of emission
inventory should be borne in mind when understanding the uncertainty of the
result. As a comparison, the traditional method uses more variables (e.g.,
NH4+, NO3-, SO42-, EC, and OC) in the
calculation, which may bring in large uncertainties to the result. For
example, Schmid et al. (2001) reported that the RSD in the EC measurement could
reach 36.6 %–45.5 %, which would cause large
uncertainties in calculating the ratio of OC ∕ EC and consequently cause
the SOC to be biased by up to 64 % (Guo et al., 2014). Meanwhile, the
approach to estimating SOA by multiplying the SOC by an empirical coefficient
is still controversial (Docherty et al., 2008). However, it is difficult to
make a direct comparison of uncertainties between the Si-dilution method and
traditional multitracer method due to the absence of error information from the
emission inventory. Despite that, considering that the present method uses
only a single tracer, it seems easier to control the uncertainties with the
present method than with the traditional multitracer method.

We also compared the estimate results of the Si-dilution method with the
traditional multitracer method (Table S2). As shown in Fig. 4b–c, generally, the SA contribution estimated by the Si-dilution method was
slightly higher than that obtained by the traditional method. Such a
difference can be explained by the different strategies of the methods: the
traditional method is based on a limited number of species rather than all active secondary
species in PM2.5, and thus the SA may be prone to underestimation,
while the Si-dilution method only uses an inactive species (i.e., Si), and
possible loss of Si-containing components during the sample pretreatment
procedures may cause the result to be overestimated. Despite that, the
results between the two methods were close. These two methods were further
compared with a special haze episode during 16 to 17 October in 2013 in
Beijing. During this episode, the PM2.5 concentration burst from 26.1
to 197.8 µg m−3 (Fig. 4d). The Si-dilution method showed that the
SA contribution increased remarkably from 35.6 % for 16 October to 75.0 %
for 17 October, which was highly consistent with that obtained with the
traditional method (Fig. 4e). This could also verify the accuracy of the
Si-dilution method.

Figure 5Correlation analysis of Si isotopic composition
(δ30Si) of PM2.5 with the secondary species
SO42-(a), NO3-(b), NH4+(c), and SOC (d).

In addition to the SA formation estimation, the correlation analysis of Si
isotopic composition of PM2.5 with the SA can further reveal the
sources of the precursors of SA. Note that the Si isotopic composition is
independent of Si abundance of PM2.5, and it can actually reflect the
primary sources of PM2.5, because different primary sources have
different Si isotopic signatures (e.g., coal burning and industrial emission
are 30Si-depleted sources, while vehicle emission and dust are
30Si-enriched sources) (Lu et al., 2018). From Fig. S5, the secondary aerosols were negatively correlated with the
δ30Si of PM2.5, suggesting that 30Si-depleted primary
sources (e.g., coal burning and industrial emission) contributed more
precursors to secondary aerosols than 30Si-enriched sources.
Specifically, from Fig. 5a, the δ30Si showed a declining trend
with the increase in SO42- concentration (P=0.006), suggesting
that the 30Si-depleted primary sources contributed more precursor
(i.e., SO2) to the SO42- species than 30Si-enriched
sources. This was consistent with the fact that SO2 mainly derives from
coal burning, which is a 30Si-depleted source (Lu et al., 2018).
The NH4+ and SOC concentration was also negatively correlated with
the δ30Si (Fig. 5c, d), suggesting that the precursors of
these secondary species were also largely contributed by 30Si-depleted
primary sources. However, the NO3- concentration did not show any
correlation with the δ30Si (P=0.63; Fig. 5b). This could be
explained by the fact that the sources of NO3- were more uncertain
and involved both 30Si-enriched (e.g., vehicle exhaust) and
30Si-depleted primary sources (e.g., power plants) (Seinfeld and
Pandis, 2016).

To further show the correlation of Si isotopic signatures with the SA
precursors, we also analyzed two typical haze episodes in 2013 in Beijing
(Fig. S6). In the episode during 30 November to 1 December 2013 (Fig. S6a–j),
the PM2.5 rapidly increased from 26.1 to 197.8 µg m−3, during which the SIA, SOA, and their major constituents
(i.e., NH4+, NO3-, SO42-, EC, and SOC)
synchronously increased and the SA precursors (NOx and SO2) also
showed a rapid rise. Meanwhile, the δ30Si of PM2.5 shifted
negatively with the SA formation, suggesting that the SA precursors were
largely contributed by 30Si-depleted sources. In another haze episode
during 6 to 7 December 2013 (Fig. S6k–t), similar phenomena
were observed, and the δ30Si of PM2.5 shifted negatively
with the SA formation. Thus, all results mentioned above suggested that
30Si-depleted primary sources (e.g., coal burning and industrial
emission) contributed more SA precursors than 30Si-enriched sources.
This deduction was consistent with previous knowledge that coal burning and
industrial emission contributed more precursors to SA (e.g., SO2,
NOx, and VOCs) (Sun et al., 2016).
Accordingly, we show that the Si isotopic composition not only indicates the
primary sources, but can also reveal the sources of precursors of secondary
species of PM2.5.

3.7 Implications for air pollution control policies

The SA contribution obtained here (79.2±26.1 % for all of 2013
and 88.7±8.9 % for January 2013; Fig. 4) was higher overall than that
in a previous report (30 %–77 % for four cities in China in January 2013), probably due
to the different sampling dates (Huang et al., 2014).
It is worth noting that these results have been verified by two independent methods
(Fig. 4). Thus, more strict policies should be enforced to reduce the
emission of secondary particle precursors including SO2, NOx, and
VOCs for haze pollution control, especially for NOx and VOCs, which have
not aroused enough attention in current pollution control strategies
(Wang et al., 2013). Furthermore, the 30Si-depleted
primary sources (e.g., coal burning and industrial emissions) should be
regulated because they contribute considerably to both primary
particles and secondary particle precursors.

In summary, we investigated the role of Si during aerosol secondary
formation and showed that the high abundance and chemical inactiveness of Si
make it a new conservative tracer in investigating the aerosol formation
process. Based on the Si-dilution effect, we have proposed a new method to
estimate the secondary aerosol contribution to PM2.5 by using Si as a
single tracer. In addition, the sources of the precursors of secondary
aerosols can be revealed by the correlation analysis of secondary aerosols
with the Si isotopic composition of PM2.5. Overall, this study not only
enriches our understanding of the role of Si during the aerosol formation
process, but also adds a new alternative tool into the toolbox of aerosol
chemistry research. However, it should be noted that this method is expected
to be useful only in regions where crustal particles represent a measurable
amount of PM2.5, and in areas dominated by biogenic aerosols (e.g.,
forests) its usefulness is still questionable. Meanwhile, some measures may
further improve the accuracy of the method, such as increasing the size and
representativeness of primary source sample sets. Considering the
interregional transport of PM2.5, future efforts should also be made to
include primary sources and emission inventories of adjacent cities and regions in
the calculation.

QL designed the research. GJ supervised the project. DL performed most of
experiments. JT measured the concentration of secondary species and
calculated the secondary aerosol contribution using the traditional method.
XY helped with the measurements. XS measured the atmospheric concentration
of SO2 and NOx and RH. QL and DL analyzed the data and wrote the
paper.

This work was financially supported by the National Natural Science
Foundation of China (no. 21825403, 91843301, 91543104), the Chinese Academy
of Sciences (XDB14010400, QYZDB-SSW-DQC018), and the National Basic Research
Program of China (2015CB931903, 2015CB932003).

Atkinson, R.: Kinetics of the gas-phase reactions of a series of
organosilicon compounds with OH and NO3 Radicals and O3 at 297±2 K, Environ. Sci. Technol., 25, 863–866, https://doi.org/10.1021/Es00017a005, 1991.

We investigated for the first time the role of Si during secondary formation process of PM2.5. We show the noncorrelation of Si with the secondary aerosol (SA) formation in Beijing, which reveals a new conservative tracer for aerosol chemistry. The SA contribution can be estimated by using Si as a single tracer instead of commonly used multiple chemical tracers. The correlation analysis of SA with the Si isotopic composition of PM2.5 can also reveal the sources of the precursors of SA.

We investigated for the first time the role of Si during secondary formation process of PM2.5....